Mistral • text
This is Mistral AI's flagship model, Mistral Large 2 (version mistral-large-2407). It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more. Read the launch announcement [here](https://mistral.ai/news/mistral-large-2407/)....
Context Window
131K tokens
Input Price/1M
$2.00
Output Price/1M
$6.00
Parameters
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Mistral Large 2 (Jul '24) results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| SciCode | 27.0 | 100.0 | — |
| LiveCodeBench | 27.0 | 100.0 | Artificial Analysis official API |
| AA Coding Index | 13.8 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU-Pro | 68.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA-LCR | 2.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MATH-500 | 71.4 | 100.0 | Artificial Analysis official API |
| AIME 2025 | 0.0 | 100.0 | Artificial Analysis official API |
| AA Math Index | 0.0 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Intelligence Index | 13.0 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU Pro | 68.3 | 100.0 | Artificial Analysis official API |
| GPQA Diamond | 47.0 | 100.0 | Artificial Analysis official API |
| IFBench | 32.0 | 100.0 | — |
| HLE | 3.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Tau²-Bench | 33.0 | 100.0 | — |
Mistral Large 2 (Jul '24) is an AI model developed by Mistral, classified as a text model. It focuses on text processing and natural language generation. As a proprietary model, it is available via Mistral's cloud API. With a context window of 131K tokens, it is suitable for processing long documents such as contracts, books, and complete codebases.
Mistral Large 2 (Jul '24) is usage-based, priced at $2/1M input tokens and $6/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. The mid-range pricing balances quality and cost for most professional applications.
Mistral Large 2 (Jul '24) was evaluated on 14 different benchmarks, covering categories like Coding, Knowledge, Long Context, Math, overall, Reasoning, Tool Use. Results show moderate performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Mistral Large 2 (Jul '24) specializes in text, offering advanced capabilities for creating and processing text content.
In the 2026 AI model ecosystem, Mistral Large 2 (Jul '24) competes directly with similarly capable models. Mistral competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Mistral Large 2 (Jul '24) is an AI model developed by Mistral. It is a text model.
Mistral Large 2 (Jul '24) costs $2/1M input tokens and $6/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
In available benchmarks, Mistral Large 2 (Jul '24) scored: SciCode: 27/100, LiveCodeBench: 27/100, AA Coding Index: 13.8/100. See the full table above for a detailed comparison.
No, Mistral Large 2 (Jul '24) is a proprietary model from Mistral. It is available via cloud API. For open source alternatives, check our open source model ranking.
Mistral Large 2 (Jul '24) excels at general-purpose language tasks. With its large context window, it handles long documents, codebases, and extended conversations. It supports tool calling for API integrations and automation.
Last updated: June 01, 2026 • View methodology →